
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}





class     __UpperCamelCase (							SCREAMING_SNAKE_CASE_       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''table-transformer'''
     lowerCAmelCase_							   =			['''past_key_values''']
     lowerCAmelCase_							   =			{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }



     def __init__(				self							:							str     ,						_A							:							Tuple=True     ,						_A							:							List[str]=None     ,						_A							:							List[Any]=3     ,						_A							:							List[str]=100     ,						_A							:							str=6     ,						_A							:							List[str]=2048     ,						_A							:							Any=8     ,						_A							:							List[str]=6     ,						_A							:							Tuple=2048     ,						_A							:							Any=8     ,						_A							:							str=0.0     ,						_A							:							str=0.0     ,						_A							:							List[str]=True     ,						_A							:							Optional[int]="relu"     ,						_A							:							str=256     ,						_A							:							Tuple=0.1     ,						_A							:							int=0.0     ,						_A							:							Optional[Any]=0.0     ,						_A							:							List[Any]=0.02     ,						_A							:							Union[str, Any]=1.0     ,						_A							:							Any=False     ,						_A							:							Any="sine"     ,						_A							:							Optional[int]="resnet50"     ,						_A							:							Optional[Any]=True     ,						_A							:							Any=False     ,						_A							:							Optional[Any]=1     ,						_A							:							Union[str, Any]=5     ,						_A							:							str=2     ,						_A							:							int=1     ,						_A							:							Optional[int]=1     ,						_A							:							List[str]=5     ,						_A							:							List[str]=2     ,						_A							:							Optional[Any]=0.1     ,						**_A							:							List[str]     ,						):







       """simple docstring"""




       if backbone_config is not None and use_timm_backbone:
         raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )

       if not use_timm_backbone:
         if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
         elif isinstance(snake_case__     ,						snake_case__ ):
           __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		backbone_config.get('''model_type''' )
           __SCREAMING_SNAKE_CASE      :	List[str]					=		CONFIG_MAPPING[backbone_model_type]
           __SCREAMING_SNAKE_CASE      :	List[str]					=		config_class.from_dict(snake_case__ )
         # set timm attributes to None
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		None, None, None

       __SCREAMING_SNAKE_CASE      :	str					=		use_timm_backbone
       __SCREAMING_SNAKE_CASE      :	str					=		backbone_config
       __SCREAMING_SNAKE_CASE      :	Dict					=		num_channels
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_queries
       __SCREAMING_SNAKE_CASE      :	Dict					=		d_model
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		encoder_ffn_dim
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		encoder_layers
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		encoder_attention_heads
       __SCREAMING_SNAKE_CASE      :	str					=		decoder_ffn_dim
       __SCREAMING_SNAKE_CASE      :	List[str]					=		decoder_layers
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		decoder_attention_heads
       __SCREAMING_SNAKE_CASE      :	List[str]					=		dropout
       __SCREAMING_SNAKE_CASE      :	Tuple					=		attention_dropout
       __SCREAMING_SNAKE_CASE      :	str					=		activation_dropout
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		activation_function
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		init_std
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		init_xavier_std
       __SCREAMING_SNAKE_CASE      :	Any					=		encoder_layerdrop
       __SCREAMING_SNAKE_CASE      :	Tuple					=		decoder_layerdrop
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		encoder_layers
       __SCREAMING_SNAKE_CASE      :	Dict					=		auxiliary_loss
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		position_embedding_type
       __SCREAMING_SNAKE_CASE      :	Dict					=		backbone
       __SCREAMING_SNAKE_CASE      :	Dict					=		use_pretrained_backbone
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		dilation
       # Hungarian matcher
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		class_cost
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		bbox_cost
       __SCREAMING_SNAKE_CASE      :	Dict					=		giou_cost
       # Loss coefficients
       __SCREAMING_SNAKE_CASE      :	Tuple					=		mask_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	str					=		dice_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	int					=		bbox_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	int					=		giou_loss_coefficient
       __SCREAMING_SNAKE_CASE      :	Dict					=		eos_coefficient
       super().__init__(is_encoder_decoder=snake_case__     ,						**snake_case__ )



     @property
     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       return self.encoder_attention_heads




     @property
     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       return self.d_model






class     __UpperCamelCase (							SCREAMING_SNAKE_CASE_       ):






     """simple docstring"""







     lowerCAmelCase_							   =			version.parse('''1.11'''       )



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ] )



     @property
     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       return 1e-5




     @property
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       return 12

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))